Search Results (9,515)

The construction industry remains a cornerstone of the global economy; however, it continues to face persistent challenges, including low productivity, frequent workplace accidents, and environmental degradation. This study employs a systematic literature review to explore how digital technologies can enhance these three areas in construction project management, focusing on their benefits and challenges. The study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 18 articles were retrieved from Scopus and Web of Science databases. The findings highlight Building Information Modeling (BIM) as the most influential digital innovation supporting productivity gains, improved safety standards, and progress towards sustainable practices. Among the three focus areas, productivity remains the most extensively investigated, while sustainability is relatively underexplored. The identified benefits include increased productivity, enhanced safety, improved risk management, data-driven decision-making, improved sustainability, real-time monitoring, and stronger collaboration. Conversely, significant barriers include high implementation and training costs, data privacy concerns, a limited number of skilled workers, and resistance to change among construction stakeholders. The review emphasizes the need for further empirical studies that investigate underrepresented technologies and regional contexts. It further suggests that industry practitioners and policymakers should prioritize digital capacity building, policy incentives, and regulatory frameworks to strengthen the sustainable digital transformation of construction project management. This review presents a unique, integrated perspective by synthesizing outcomes related to productivity, safety, and sustainability. It not only delineates critical research gaps but also provides actionable guidance for industry practitioners and policymakers by prioritizing strategic areas such as digital capacity building, policy incentives, and regulatory frameworks. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a growing global health challenge, driven in part by how antibiotics are accessed, distributed, and used within complex value chains. In peri-urban India, these supply chains involve a range of formal and informal actors and practices, making them a critical yet underexamined focus for antimicrobial stewardship efforts. While much research has focused on the manufacturing and regulatory end, less is known about how antibiotics reach consumers in rural and peri-urban settings. This study aimed to map the human antibiotic value chain in West Bengal, India, and to analyse how formal and informal governance structures influence antibiotic use and stewardship. Methods: This qualitative study was conducted in two Gram Panchayats in South 24 Parganas district, West Bengal, India. Semi-structured interviews were carried out with 31 key informants, including informal providers, medical representatives, wholesalers, pharmacists, and regulators. Interviews explored the structure of the antibiotic value chain, actor relationships, and regulatory mechanisms. Data were analysed thematically using a value chain governance framework and NVivo 12 for coding. Results: The antibiotic value chain in rural West Bengal is highly fragmented and governed by overlapping formal and informal rules. Multiple actors—many holding dual or unofficial roles—operate across four to five tiers of distribution. Informal providers play a central role in both prescription and dispensing, often without legal licences but with strong community trust. Informal norms, credit systems, and market incentives shape prescribing behaviour, while formal regulatory enforcement is inconsistent or absent. Conclusions: Efforts to promote antibiotic stewardship must move beyond binary formal–informal distinctions and target governance structures across the entire value chain. Greater attention should be paid to actors higher up the chain, including wholesalers and pharmaceutical marketing networks, to improve stewardship and access simultaneously. This study highlights how fragmented governance structures, overlapping actor roles, and uneven regulation within antibiotic value chains create critical gaps that must be addressed to design effective antimicrobial stewardship strategies. Full article

Agronomic management directly influences soil and berry quality in vineyards, a crop of global relevance. However, some knowledge gaps regarding the effects of management practices in traditional vineyards of the Itata Valley in Chile remain. This study evaluated the impact of contrasting management systems: non-managed País (PA), conventionally managed País (CPA), organically managed Cinsault (OCI) and organically managed Carmenere (OCA), on soil bioindicators, chemical composition and berry rheological properties. The results showed that organic management, such as OCA, resulted in 96% and 95% higher dehydrogenase and urease activities, respectively, while OCI exceeded CPA by 86% and 173% in arylsulfatase and phosphatase activities, respectively. The CPA treatment exhibited significantly higher available nitrogen compared with PA (231%), OCI (509%) and OCA (236%), as well as greater available phosphorus than OCI (503%) and OCA (413%). Regarding berry rheology, OCA displayed the highest pulp viscosity compared to OCI, although the differences among treatments were not statistically significant. Multivariate analysis associated CPA with higher soil chemical fertility, whereas organic systems (OCI and OCA) were related to greater soil bioactivity and fruit viscosity. Therefore, organic management is recommended to improve soil biological functionality and fruit structural stability, contributing to the long-term sustainability of vineyards in the valley. Full article

The digital transformation of cultural heritage has expanded the availability of data while revealing structural forms of incompleteness. This study investigates how missing data are conceptualised in the scientific and design literature on digital cultural heritage and how Advanced Design can transform absence into a resource. The research combines a critical thematic review of peer-reviewed publications from 2010 to 2025 with Research through Design practices and case studies developed within the PNRR CHANGES project. The analysis identifies three main configurations of absence: processual gaps arising along the data lifecycle, epistemic exclusions embedded in standards and knowledge models, and projectual shortcomings related to governance and participation. Based on these findings, a design taxonomy and an operational model are proposed, linking each form of absence to specific levers of intervention, such as transparency of workflows, community-grounded annotation and narration, collaborative metadata writing, and long-term maintenance practices. The results show that Advanced Design provides an infrastructural and reflective framework capable of connecting technical processes, cultural interpretation, and community involvement. The study concludes that incompleteness, rather than a defect, can act as a generative condition for digital heritage, fostering more inclusive, situated, and transformative design practices. Full article

This narrative review examines the complexities of medical uncertainty in veterinary practice, highlighting its significant implications for clinical reasoning and decision-making. Veterinary professionals face inherent uncertainties due to factors such as biological variability, incomplete knowledge, and the pressures of rapidly evolving practices. The distinction between clinical ambiguity and medical uncertainty is crucial, as it informs the coping strategies employed by veterinarians. While uncertainty is often viewed negatively, it can stimulate curiosity and enhance problem-solving capabilities. This review categorizes uncertainty into aleatoric and epistemic types, offering insights into their origins and impacts on veterinary professionals and client interactions. The dynamic nature of uncertainty influences both immediate clinical encounters and long-term professional development, with varying effects based on individual tolerance levels and situational stakes. Despite the growing body of literature on uncertainty, veterinary education often neglects to address this critical aspect, leading to a gap in metacognitive competencies among practitioners. We have included a case example that offers explicit guidelines on the application of the Five Microskills model of clinical teaching. This model is proposed to assist veterinary professionals in effectively managing uncertainty. To enhance the quality of veterinary care, there is an urgent need to integrate uncertainty management into veterinary curricula and ongoing professional development. By fostering an environment that acknowledges and addresses uncertainty, veterinary professionals can improve their clinical reasoning, strengthen client relationships, and ultimately enhance patient outcomes. This review advocates for the adoption of evidence-based practices and collaborative approaches to navigate the complexities of uncertainty, ensuring high standards of care in veterinary medicine. Full article

As part of transportation planning processes, simulators are used to mirror real-world situations to test new policies and evaluate infrastructure changes. In practice, simulator selection has often been based on availability rather than on technical suitability, particularly for microscopic-scale applications. In this study, a quantitative methodology focusing on simulation runtime, memory usage, runtime consistency, travel time, safe gap distance, and scalability is proposed. A combined index was developed to assess simulators across different scales and traffic densities. VISSIM, SUMO, and MATSim were tested, and the results indicate that SUMO and MATSim demonstrate strong performance in runtime and memory usage. In large-scale scenarios, both simulators proved suitable for high-demand simulations, with MATSim exhibiting greater scalability. VISSIM matches real-world travel times more closely and fairly handles realistic safe gap distances, making it more suitable for less dense, detailed, microscopic simulations. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

Higher education institutions have been called to step up and contribute towards the United Nations Sustainable Development Goals (SDGs). Much research has been conducted in the areas of sustainable development and responsible management education. However, global progress towards achieving the SDGs has been slow. This paper scrutinizes foreign (subsidiary) and local business schools operating in Singapore by exploring the factors that influence students’ consideration of a program of study and what is important to the student experience, particularly from a sustainability perspective. An online survey questionnaire was distributed, and 139 participants completed it. Results suggest no significant difference in attitudes between genders and local and international students. However, all students are concerned and expect the school to provide support for mental well-being. The analysis also revealed that sustainability perceptions and awareness are low, and that students received very little education on sustainability. Despite extensive research on sustainable development and responsible management education, a significant gap remains between theory and practice, primarily due to the lack of translation of theory into practical applications. Full article

Large language models (LLMs) increasingly serve as decision-support systems across linguistically diverse populations, yet whether they reason consistently across languages remains underexplored. We investigate whether LLMs exhibit language-dependent preferences in distributive justice scenarios and whether domain persona prompting can reduce cross-linguistic inconsistencies. Using six behavioral economics scenarios adapted from canonical social preferences research, we evaluate Gemini 2.0 Flash across English and Korean in both baseline and persona-injected conditions, yielding 1,201,200 observations across ten professional domains. Results reveal substantial baseline cross-linguistic divergence: five of six scenarios exhibit significant language effects (9–56 percentage point gaps), including complete preference reversals. Domain persona injection reduces these gaps by 62.7% on average, with normative disciplines (sociology, economics, law, philosophy, and history) demonstrating greater effectiveness than technical domains. Systematic boundary conditions emerge: scenarios presenting isolated ethical conflict resist intervention. These findings parallel human foreign-language effects in moral psychology while demonstrating that computational agents are more amenable to alignment interventions. We propose a compensatory integration framework explaining when professional framing succeeds or fails, providing practical guidance for multilingual LLM deployment, and establishing cross-linguistic consistency as a critical alignment metric. Full article

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications. Full article

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

This review explores the intersection of gender, geography, and sustainability by examining the role of women in the blue economy across Europe’s Outermost Regions (ORs). Despite growing recognition of the blue economy’s role in sustainable development, there is limited understanding of how women participate in these sectors at the geographic periphery of the European Union. Using publicly available data from Eurostat, INSEE, ISTAC, and other national portals, we analyze employment patterns through a gender lens, supported by qualitative insights from case studies in regions such as the Azores, Réunion, and Guadeloupe. Due to the scarcity of disaggregated blue economy data, general labor force participation is used as a proxy, highlighting both opportunities and visibility gaps. Theoretically grounded in feminist political ecology and intersectionality, the review identifies key barriers, including data invisibility, occupational segregation, and structural inequalities, as well as resilience enablers such as women-led enterprises and policy interventions. We conclude with targeted recommendations for research, policy, and practice to support inclusive blue economies in ORs, emphasizing the need for better data systems and gender-sensitive coastal development strategies. Full article

Romania faces a double challenge in the swine production sector. On one hand, the European Union’s environmental agenda demands that member states drastically reduce both the carbon footprint and the use of antibiotics in animal husbandry by 2030. On the other hand, the Romanian swine industry still grapples with long-standing internal issues such as excessive fragmentation, a strong dependence on imported piglets and feed materials, and a clear shortage of modern management experience. This study set out to explore how the ADKAR model can serve as a structured approach to help commercial swine farms in Romania transition toward sustainability. To gather relevant data, researchers distributed a five-point Likert-scale questionnaire to 83 farm managers, out of the 361 officially registered commercial swine farms. The instrument was designed to assess how each farm positioned itself across the five ADKAR dimensions. The results revealed that most Romanian farm managers are highly aware of the need for change and show a generally positive attitude toward adopting sustainable practices. However, there remain considerable knowledge gaps and practical limitations, which continue to act as major barriers to effective implementation. The composite ADKAR-S Index, which measures the “sustainability maturity” of each farm, displayed a strong positive correlation with economic performance, particularly the profit margin (r ≈ 0.45, p < 0.001), and a significant negative correlation with antimicrobial use (r ≈ −0.50, p < 0.001). Simply put, farms that are better prepared for organizational transformation tend to perform better financially while also reducing their environmental footprint. The findings suggest that policy efforts should prioritize human capital development, especially through training programs and reinforcement systems such as continuous monitoring and staff incentives, to ensure that sustainable practices are not only adopted but also maintained in the long run. Full article